Journal of the International Neuropsychological Society

Research Articles

Diagnostic utility of the NAB List Learning test in Alzheimer’s disease and amnestic mild cognitive impairment

BRANDON E. GAVETTa1, SABRINA J. POONa1, AL OZONOFFa2, ANGELA L. JEFFERSONa1, ANIL K. NAIRa1, ROBERT C. GREENa1a3a4 and ROBERT A. STERNa1 c1

a1 Department of Neurology, Boston University School of Medicine, Boston, Massachusetts

a2 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts

a3 Department of Medicine (Genetics Program), Boston University School of Medicine, Boston, Massachusetts

a4 Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts

Abstract

Measures of episodic memory are often used to identify Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The Neuropsychological Assessment Battery (NAB) List Learning test is a promising tool for the memory assessment of older adults due to its simplicity of administration, good psychometric properties, equivalent forms, and extensive normative data. This study examined the diagnostic utility of the NAB List Learning test for differentiating cognitively healthy, MCI, and AD groups. One hundred fifty-three participants (age: range, 57–94 years; M = 74 years; SD, 8 years; sex: 61% women) were diagnosed by a multidisciplinary consensus team as cognitively normal, amnestic MCI (aMCI; single and multiple domain), or AD, independent of NAB List Learning performance. In univariate analyses, receiver operating characteristics curve analyses were conducted for four demographically-corrected NAB List Learning variables. Additionally, multivariate ordinal logistic regression and fivefold cross-validation was used to create and validate a predictive model based on demographic variables and NAB List Learning test raw scores. At optimal cutoff scores, univariate sensitivity values ranged from .58 to .92 and univariate specificity values ranged from .52 to .97. Multivariate ordinal regression produced a model that classified individuals with 80% accuracy and good predictive power. (JINS, 2009, 15, 121–129.)

(Received June 11 2008)

(Reviewed October 08 2008)

(Accepted October 10 2008)

Correspondence:

c1 Correspondence and reprint requests to: Robert A. Stern, Alzheimer’s Disease Clinical and Research Program, Boston University School of Medicine, Robinson 7800, 72 East Concord Street, Boston, MA 02118-2526. E-mail: bobstern@bu.edu

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